Severity: Warning
Message: file_get_contents(https://...@gmail.com&api_key=61f08fa0b96a73de8c900d749fcb997acc09&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 197
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 197
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 271
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3165
Function: getPubMedXML
File: /var/www/html/application/controllers/Detail.php
Line: 597
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 511
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 317
Function: require_once
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Dynamic modeling of mechanisms driving volatile fatty acid and hydrogen production in the rumen microbial ecosystem contributes to the heuristic prediction of CH emissions from dairy cattle into the environment. Existing mathematical rumen models, however, lack the representation of these mechanisms. A dynamic mechanistic model was developed that simulates the thermodynamic control of hydrogen partial pressure ( [Formula: see text] ) on volatile fatty acid (VFA) fermentation pathways via the NAD to NADH ratio in fermentative microbes, and methanogenesis in the bovine rumen. This model is unique and closely aligns with principles of reaction kinetics and thermodynamics. Model state variables represent ruminal carbohydrate substrates, bacteria and protozoa, methanogens, and gaseous and dissolved fermentation end products. The model was extended with static equations to model the hindgut metabolism. Feed composition and twice daily feeding were used as model inputs. Model parameters were estimated to experimental data using a Bayesian calibration procedure, after which the uncertainty of the parameter distribution on the model output was assessed. The model predicted a marked peak in [Formula: see text] after feeding that rapidly declined in time. This peak in [Formula: see text] caused a decrease in NAD to NADH ratio followed by an increased propionate molar proportion at the expense of acetate molar proportion, and an increase in CH production that steadily decreased in time, although the magnitude of increase for CH emission was less than for [Formula: see text] . A global sensitivity analysis indicated that parameters that determine the fractional passage rate and NADH oxidation rate altogether explained 86% of the variation in predicted daily CH emission. Model evaluation indicated over-prediction of in vivo CH emissions shortly after feeding, whereas under-prediction was indicated at later times. The present rumen fermentation modeling effort uniquely provides the integration of the [Formula: see text] controlled NAD to NADH ratio for dynamically predicting metabolic pathways that yield VFA, H and CH.
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http://dx.doi.org/10.1016/j.jtbi.2019.08.008 | DOI Listing |